Socio-economic drought impacts often occur concomitantly across multiple sectors, leading to more severe consequences than if they affected single sectors. Improved management of such disasters requires cross-sectoral impact assessments and analyses. As such, analyzing how regions are affected by multiple impacts can provide crucial information for mitigating their consequences. Here, we characterize the multivariate distributions of socio-economic drought impacts. Our aim is to understand patterns by which diverse drought impacts co-occur. We introduce the concept of drought impact profiles, which describe characteristic distributions of co-occurring impacts. To this end, we use a unique spatio-temporal dataset generated with text mining and machine learning applied to newspaper articles. This dataset describes reported socio-economic drought impacts along seven categories (agriculture, forestry, fires,  social, aquaculture, livestock, waterways) in Germany between 2000-2022. We combine several dimensionality reduction algorithms (PCA, ISOmap, self-organizing maps) to generate robust and interpretable representations of the drought impacts. Our results show characteristic patterns for both particular drought events and regions. Also, the applied methods provide a low-dimensional representation of the multivariate socio-economic drought impacts. This research provides a methodological contribution to the holistic, empirical investigation of co-occurring drought impacts. The proposed methods can inform risk models, and policy-makers on the urgency of cross-sectoral governance approaches. Also, the proposed method could apply to other hazards or compound events.

How to cite: Sodoge, J., Kuhlicke, C., Mahecha, M., and de Brito, M.: Drought impact profiles: Analyzing multivariate socio-economic drought impacts using nonlinear dimensionality reduction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-286, https://doi.org/10.5194/egusphere-egu23-286, 2023.

Climate change is projected to increase the frequency and intensity of future droughts particularly affecting the most low-income countries directly dependent on local rainfed food security and livelihoods. Drought risk and its related impacts depend on the drought hazard, the exposure and the vulnerability of the different socioeconomic sectors and/or ecosystems as well as the adaptive capacity of affected locations. The Horn of Africa, which includes Ethiopia, is currently experiencing one of the most severe droughts in the last 40 years. This study applies a storyline approach to investigate changes in drought risk for Ethiopia combining vulnerability, hazard and adaptive capacity information for current and future projected climatic and socio-economic conditions using a subnational level composite indicator. For our analysis, we define drought based on the Standardised Precipitation-Evapotranspiration Index (SPEI) which characterises the deficits in local water availability based on the precipitation and potential evapotranspiration. SPEI is computed using bias corrected Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) project based on the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Drought vulnerability assessment is carried out combining exposure, adaptive capacity and sensitivity indicators, using INFORM index developed by the Joint Research Centre of the European Commission to support humanitarian crisis and disaster decision-making. The analysis shows that future drought will increase people in need of food assistance both under current population and future population projections. If humanitarian aid and assistance are maintained at recent historical levels, these findings show a substantial increase in the required amounts. These conditions are exasperated when humanitarian access is impeded by local conditions such as the current conflict in Ethiopia, when imports are reduced by crises such as those associated with the Russian invasion of the Ukraine, and by pandemics such as COVID-19. Climate change mitigation is shown to reduce the vulnerability of Ethiopia through a reduction in drought hazard frequency and intensity. The framework presented in this study can be used as a policymaking tool to provide information on how to better prioritize future loss and damage funds and adaptation and mitigation investments to reduce population vulnerability and exposure.

How to cite: Bovienzo, D., Marzi, S., Monteleone, L., Mysiak, J., and Pal, J.: Current and future evolution of drought risk in Ethiopia: A framework to inform disaster risk reduction and climate change adaptation policies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14314, https://doi.org/10.5194/egusphere-egu23-14314, 2023.

Individual adaptation is essential for achieving community resilience as well as coping with residual risks that have not been addressed by current structural schemes for reducing flood risks. At the same time, it also implies that individuals should have the resources and capacity to protect themselves. So far, this has been interpreted in the social vulnerability concept as accounting only for income, wealth, or other materially relevant factors, showing how much vulnerable people are exposed to more risk. However, individual behavioural adaptability has hardly been included in the current vulnerability assessment.

In light of this, this study proposes a novel way to expand and link social classes using well-established social vulnerability indicators (i.e. income, education, and job status) with socio-psychological and lifestyle elements theoretically and empirically known to influence individual protective behaviour. We conducted a bias-adjusted three-step Latent Class Analysis (LCA) with covariates (socio-psychological and lifestyle elements) and distal outcomes (adaptive behaviour). A household survey (n = 1,753) conducted between June and July 2020 in 11 cities in Saxony, Germany, was used.

The preliminary result shows that socio-psychological and cultural factors that influence individual decision-making on proactive adaptive behaviour co-vary with social classes based on their resource endowment. It also revealed that the lower class tends to have less implementation of costly adaptation methods, for example, structural measures on housing, while less costly measures did not make a significant difference. As a result, we recommend that, in addition to the lack of material endowment, which can be associated with an increased risk of exposure, individual inaction of protective behaviour motivated by socio-psychological traits be considered for social vulnerability.

How to cite: Han, S., Masson, T., Köhler, S., and Kuhlicke, C.: Bridge the gap: Linking social vulnerability and adaptive behaviour, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5148, https://doi.org/10.5194/egusphere-egu23-5148, 2023.

Despite major advancements in climate modeling, weather forecasting, and emergency preparedness, deadly floods continue to have a global reach, impacting Eastern Kentucky, USA (July 2022), Assam, India (2022), Cape Town, South Africa (2022), and Insul, Germany (July 2021) to name just a few. The goal of this work is to quantify and forecast in near-real time a flood’s impact at high spatial resolution by estimating how a household’s accessibility to critical infrastructure changes during and immediately after a storm. Our approach consists of a static transportation assignment cost function that solves for the user equilibrium traffic solution. By overlaying the road network with a near-real-time pluvial and fluvial inundation estimate, we estimate the degree to which flooding impacts households’ likely travel patterns to critical resources. The output consists of demand information on both the road and resource infrastructure networks, which we translate into resiliency and redundancy metrics. Our goal for this model is for it to be able to be rapidly deployed across the USA and potentially abroad to better serve communities who would otherwise not have access to such research and information tools. We present a case-study for Austin, Texas as a proof of concept and to highlight the critical decision-making information our approach can provide to those who need it most including emergency responders, flood managers, and residents themselves. Through this network approach, we can estimate who loses access to critical resources completely, whose access has diminished, how resource distribution is or isn’t equitable, hot spot nodes to prioritize remediation, and more. Our approach uses only open-source information including infrastructure, Earth observation, and point measurement data in our multilayer network. This data requirement allows our model to potentially be applicable in numerous regions across the globe. Our future work will explore using the network insights from this model in a dynamic model of adaptive capacity and human infrastructure. This will provide further insights on socio-hydrological interactions and how varying emergency response policies, government interventions, and human trends might impact the recovery trajectories of different communities.

How to cite: Preisser, M., Passalacqua, P., and Bixler, R. P.: A network-based disaster resilience metric for estimating individuals’ loss of access to critical resources during flooding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9923, https://doi.org/10.5194/egusphere-egu23-9923, 2023.

Human activities have a profound impact on climate and hydrological processes, contributing to changes in the frequency and severity of hydrological extremes and, consequently, growing socioeconomic vulnerability [1]. Rising sea levels, continuous urban development in low-lying coastal areas, and corresponding changes in flood risk have resulted in devastating flood impacts. Different Flood Risk Management (FRM) strategies have been adopted in various socioeconomic contexts and spatiotemporal scales, the most prevalent being structural protection. In recent years, numerous scholars have raised concerns about this approach, as studies have shown that increasing protection levels can increase socioeconomic vulnerabilities e.g., [2]. FRM strategies alter the dynamics of risk manifested in sociohydrological systems, which must be disentangled to avoid unintended consequences.
In the “Cities and rising sea levels” project, scientists from different research disciplines, including hydrology, architecture, landscape architecture, and urban planning, collaborate to tackle these challenges. Combining multidisciplinary knowledge has been central to exploring the cross-sectoral processes involved in FRM. In the present study, we focused on (1) uncovering the cascading effects, including unintended consequences of FRM, as well as (2) highlighting the potentials for holistic assessments of FRM strategies.
Our methods include the development of a Causal Loop Diagram (CLD) model describing critical sociohydrological processes of coastal cities operating at different spatial and temporal scales. We identified dynamic feedbacks between (1) flood risk, urban development and economic wealth, (2) flood risk, urban development and social equity, and (3) flood risk, trust in authorities, and institutional capacity, among others. . Based on the CLD, we analyzed key feedback mechanisms and their manifestation in theory and practice. Further, we explored the impacts of different FRM strategies on these feedback mechanisms to uncover differences in impacts on socioeconomic vulnerabilities and wider cross-sectoral impacts. The presentation will present and explore the conceptual model through semiquantitative analyses (Fuzzy Cognitive Maps (FCMs)) and spatiotemporal assessments using a specific case study. We aim at (1) getting case-specific insights into the dynamics produced by the local interplay of flooding events and socioeconomic processes influencing vulnerabilities, and (2) suggesting pathways for new integrated ways of FRM.

References
[1] IPCC, Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. In Press., 2022.
[2] R. W. Kates, C. E. Colten, S. Laska, and S. P. Leatherman, “Reconstruction of New Orleans after Hurricane Katrina: A research perspective,” Proc. Natl. Acad. Sci. U. S. A., vol. 103, no. 40, pp. 14653–14660, Oct. 2006, doi: 10.1073/PNAS.0605726103/ASSET/C486E9DB-5923-43C0-9881-2B57734F2A7C/ASSETS/GRAPHIC/ZPQ0410637570002.JPEG.

How to cite: Eggert, A. L., Arnbjerg-Nielsen, K., and Löwe, R.: Feedbacks between City Development and Coastal Flood Risk Management: A Systems Thinking Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3422, https://doi.org/10.5194/egusphere-egu23-3422, 2023.

The highly populated Peruvian Andes is impacted by a multitude of climate-related risks. Comprehensive climate risk management and adaptation measures can bring risks down to an acceptable level, as determined by the local population. However, increased magnitude and frequency of risks, together with the possibility of reaching adaptation limits, are hindering risk reduction. Adaptation limits are reached due to a complex interplay between socio-economic, cultural, political, institutional, technical and bio-physical factors. Whilst there is an emerging conceptual understanding of adaptation limits, there is little empirical research investigating limits in real-world settings.

The aim of this study is to identify and define the limits of adaptation on a local scale, which limits are approaching and which have already been reached. We investigate the limits of adaptation in two catchments in the Peruvian Andes. The most prevalent climate-related risks in these two regions are from glacial lake outburst floods, landslides, shifts in precipitation patterns, and glacier retreat. We use a conceptual framework developed by Juhola et al. (unpublished), and determine adaptation limits and the intolerable risks space through investigating human wellbeing, governance systems, ecosystem functions and climate hazards in the two localities. The data was collected through a thorough literature review, together with 50 semi-structured interviews conducted in May-July 2022; 28 with local residents in the Río Santa and Salkantay catchments, and 22 interviews with experts from 14 different local and national institutions and NGOs. The interviews were analysed in Atlas.ti using a content analysis approach. We emphasize the focus on basic needs and wellbeing, to encompass not only what are obvious losses from climate impacts, such as loss of life or livelihood, but also more intangible losses, such as limited mobility, loss of a social network, or loss of local knowledge. The conclusions of this study can help decision makers and practitioners improve the positive impact of future risk management and adaptation projects in the two regions.

How to cite: Hagen, I., Schnyder, S., Yanac León, I., Juhola, S., Muccione, V., and Huggel, C.: Limits of adaptation to climate-related risks in the Peruvian Andes: A case study in the Río Santa and Salkantay catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16481, https://doi.org/10.5194/egusphere-egu23-16481, 2023.

Societal resilience is built upon effective risk transfer strategies. For most developed countries in the world, insurance and reinsurance continues to be the most effective method of sharing this burden and reducing the need for state intervention. However, it’s becoming increasingly clear that the probabilistic (CAT) models used to price natural hazard risk are struggling to capture the increasingly dynamic changes of the climate and our level of interconnection.

The Gallagher Research Centre (GRC) was established recently to support reinsurance stakeholders navigate an increasingly complex risk management landscape. Though probabilistic and deterministic natural catastrophe models were first pioneered in the early 1960’s (Friedman, 1984) it wasn’t until the 1990’s, and the combined losses from Hurricane Andrew ($27.3 billion USD) and the Northridge Earthquake ($25 billion USD) that such models began to be fully embraced by the mainstream reinsurance industry (Reinsurance News, 2023).

While significant and continued progress has been made in the precision and scalability of these models in the last 30 years, climate change and an increasingly globalized world mean the relative impacts of natural hazards are becoming far more complex and diverse than most models successfully capture. This leads to an increasing basis risk and potentially less utility of the models. This session will outline the growing research concerns of focus for the GRC, including how can stochastic models built around historical periods truly capture the non-stationarity of risk we see occurring for wind and flood perils? Should models capture the seasonal dependencies between perils to more accurately price aggregate insurance risk? Should future model development focus on the compounded scenarios? 

Friedman, D. G. (1984). Natural hazard risk assessment for an insurance program. Geneva Papers on Risk and Insurance, 57-128.

Reinsurance News (2023). Last accessed 10/01/2023. https://www.reinsurancene.ws/insurance-industry-losses-events-data/

How to cite: Willis, I. and Papaspiliou, M.: The urgency for (re)insurance probabilistic (CAT) models to capture the dynamics of an increasingly interconnected world, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17449, https://doi.org/10.5194/egusphere-egu23-17449, 2023.

As industrialization and urbanization progress around the world, more complex and large-scale complex disasters are occurring, causing numerous casualties and property damage every year. As climate change gradually accelerates and its impact grows, such as recent cold waves and heavy snow in the United States and abnormal temperatures in Europe, it is difficult to predict with existing physical modeling alone. Recently, disasters are gradually expanding in the form of covering not only natural disasters but also various social disasters. Social disasters cover disasters such as fires, infectious diseases, and fine dust caused by human activities. Unlike natural disasters, it is difficult to measure numerical values and predict occurrence patterns in real time, so it is very important to respond quickly through information sharing. There is a limit to establishing the same response system globally to respond to disasters that may occur worldwide, so it is necessary to develop a platform that can quickly share cases while being economical. With the recent development of communication technology, about 70% of the world's population uses smartphones, and various unstructured data are being generated in real time through various social media channels. Individuals act as a sensor and can share their location or current situation in real time. Therefore, the purpose of this study is to develop crowd sourcing technology using social media, analyze the collected data, and present ways to use it in the event of a disaster. In this study, a platform was established to collect and analyze disaster-related SNS data such as floods, fine dust, and forest fires, and it was designed so that users could receive information through websites and apps. As a result of application to various disaster cases in Korea, the temporal and spatial correlation between disaster occurrence patterns and social media data was high, and the possibility of using initial monitoring methods was proved. This result can be applied to all disaster disasters or crimes, and it is expected to be highly useful as it can quickly verify disaster thoughts and share cases in real time.

How to cite: Lee, J. and Hwang, S.: A Study on the Monitoring of Complex Disaster Using Crowd Source Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4821, https://doi.org/10.5194/egusphere-egu23-4821, 2023.

The Disaster Risk Management Cycle (DRMC) is a common reference for the international Disaster Risk Management (DRM) community to describe the management of catastrophic anthropogenic and natural events worldwide. Implementing this approach, disaster management is described by a series of separate and consecutive phases (e.g., preparedness, response, and recovery). However, the current DRMC is not able to successfully cover the dynamics of multi-hazard risk scenarios, particularly those involving both sudden- (e.g., earthquakes or flash floods) and slow-onset hazards (e.g., pandemics or  droughts).

Starting from such a complex scenario we propose a ‘parallel phases’ DRM model accounting for the management of interacting sudden- and slow-onset hazards. The framed ‘parallel phases’ model allows to overcome the limitations of the existing models when dealing with complex multi-hazard risk conditions. We supported the identified limitations analysing Italian Red Cross data dealing with past and ongoing emergencies including the COVID-19 pandemic. Key findings from the analysis involve: (i) the spatial-temporal differences between sudden-onset events and pandemic disaster management; (ii) the high demand for emergency response resources during pandemics in comparison to other emergencies; (iii) the need for the DRM system to adjust the response to cope with the pandemic seasonality; (iv) the system over-exposure to pandemic response activities reducing the number of resources for preparedness and entering the system into an unpreparedness negative loop.

Overall, the combination of the key findings that emerged from the management of the COVID-19 pandemic in Italy brought out three main guidelines for advancing multi-hazard DRM by applying our ‘parallel phases’ model:

• Managing the system with parallel phases. A ‘parallel phases’ DRM allows the system to exploit the low emergency intensity of the slow-onset hazards seasonality for preparedness actions while also preparing for any other hazard that can have relevant impacts on the system. Such an approach allows the DRM system to escape from an unpreparedness negative loop. 
• Keeping the DRM system capacity far from depletion. The DRM system can learn how to efficiently deploy the available resources keeping its capacity far from total depletion. If the DRM system is able to save part of its capacity, it can continue with the increase of internal resources while also making them available for international mutual support in case of multi-hazard risk. Such a condition triggers a positive loop in the increase of the DRM capacity.
• Impact-based forecasting for multi-hazard disaster risk management. The implementation of multi-hazard seasonal impact-based forecasts fosters the planning of appropriate anticipatory actions, combining the prediction of slow-onsets waves with the seasonality of sudden-onsets.

Overall, the proposed ‘parallel phases’ model is able to capture the complex management dynamics to deal with the increasingly frequent slow-onset and multi-hazard events, introducing a change of perspective from the cyclic, consecutive-phases, and single-hazard DRM approach. For this reason, the ‘parallel phases’ model can strengthen and boost current and future international policies on multi-hazard DRM towards an effective implementation at a local scale.

How to cite: De Angeli, S., Terzi, S., Miozzo, D., Massucchielli, L. S., Szarzynski, J., Carturan, F., and Boni, G.: Managing the co-occurrence of natural hazards and pandemics with a new parallel phases DRM model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-567, https://doi.org/10.5194/egusphere-egu23-567, 2023.